Much of computer vision, image processing and imaging in general is predicated on the assumption that there is a single prevailing illuminant lighting a scene. However, often there are multiple lights. Common examples include outdoor scenes with cast and attached shadows, indoor office environments which are typically lit by skylight and artificial illumination and the spot-lighting used in commercial premises and galleries. Relative to these mixed lighting conditions, many imaging algorithms (based on the single light assumption) can fail. Examples of failure include the inability to track objects as they cross a shadow boundary or tracking a shadow rather than the object, an incorrect colour balance being chosen in image reproduction (e.g., when printing' photographs) and in an incorrect rendering of the information captured in a scene. The last problem is particularly acute when images containing strong shadows are reproduced. Operating under the single light assumption, the imaging practitioner can chose either to make the image brighter (seeing into the shadows) at the cost of compressing the detail in the lighter image areas or conversely keeping the bright areas intact but not bring out the shadow detail. Indeed, many photographs are a poor facsimile of the scenes we remember because our own visual system treats shadow and highlight regions in a spatially adaptive way in order to arrive at quite a different perceptual image.
There is a good deal of work in the literature for identifying illumination change in images. Most of the previous approaches work by comparing pixels (or regions) that are spatially adjacent. Rubins and Richards [1] argue that the RGBs across a shadow edge have a certain well defined relationship. When this relationship does not hold then the edge is not a shadow edge. Freeman et al. [2] learn the statistics of reflectance and illumination edges and have some success at classifying edges in images. Finlayson et al. in [3] show how a single grey scale image can be formed from a colour image where there are no edges due to liumination. Comparing edges in this image with those in the colour image is used as a means for shadow edge detection. Moreover, in [4] Freclembach and Finlayson consider how local edges can be integrated to identify coherent shadow regions. Re-integration of edges, with edges on shadow boundaries bridged across the boundary, results in a colour without shadows, to a good degree. In these methods, shadow edges are key. While this method can work well, it is far from perfect, and moreover is tailored to the shadow detection, as opposed to the illumination detection problem. Further, a region-based rather than edge-based method would provide more evidence indicating shadows.
Various embodiments described herein seek to provide a method for segmenting illumination in images.